4 papers
Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation
Pierrick Lorang, Johannes Huemer, Timothy Duggan +3
Enabling robots to learn long-horizon manipulation tasks from a handful of demonstrations remains a central challenge in robotics. Existing neuro-symbolic approaches often rely on…
Agentic LLM Planning via Step-Wise PDDL Simulation: An Empirical Characterisation
Kai Göbel, Pierrick Lorang, Patrik Zips +1
Task planning, the problem of sequencing actions to reach a goal from an initial state, is a core capability requirement for autonomous robotic systems. Whether large language mode…
Few-Shot Neuro-Symbolic Imitation Learning for Long-Horizon Planning and Acting
Pierrick Lorang, Hong Lu, Johannes Huemer +2
Imitation learning enables intelligent systems to acquire complex behaviors with minimal supervision. However, existing methods often focus on short-horizon skills, require large d…
Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study
Kai Goebel, Patrik Zips
Recent advancements in Large Language Models have sparked interest in their potential for robotic task planning. While these models demonstrate strong generative capabilities, thei…